Abstract In the current study, CuO nanoparticles were dispersed in a mixture of Ethylene Glycol-Water (60/40 wt. %) to prepare stable nanofluid in different concentrations (0.05 − 0.8 vol. %). The samples were used as the coolant fluid in a specific car radiator to evaluate the thermal performance of nanofluid and base fluid in the system. Five different and novel Machine-learning methods were applied over experimental data to predict the Nusselt number and output temperature of the coolant in the system. These methods are M5 tree regression, Linear and Cubic Multi-Variate Adaptive Regression Splines (MARS), Radial Basis Function (RBF), and Artificial Neural Network-Levenberg Marquardt Algorithm (ANN-LMA). Although all studied methods show acceptable accuracy in predicting experimental data, the ANN-LMA method in output temperature modeling and the MARS-Linear method in Nusselt number modeling has more precision.

中文翻译：

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应用 M5 树回归、MARS 和人工神经网络方法预测汽车散热器中基于 CuO 的纳米流体流的努塞尔数和输出温度

摘要 在目前的研究中，CuO 纳米颗粒分散在乙二醇 - 水（60/40 wt. %）的混合物中，以制备不同浓度（0.05 - 0.8 vol. %）的稳定纳米流体。样品被用作特定汽车散热器中的冷却液，以评估系统中纳米流体和基液的热性能。五种不同且新颖的机器学习方法应用于实验数据，以预测系统中冷却剂的努塞尔数和输出温度。这些方法是 M5 树回归、线性和三次多元自适应回归样条 (MARS)、径向基函数 (RBF) 和人工神经网络-Levenberg Marquardt 算法 (ANN-LMA)。尽管所有研究的方法在预测实验数据方面都表现出可接受的准确性，